Techniques for automatic opportunity evaluation and action recommendation engine

Abstract

Described herein are systems and techniques for identifying at-risk opportunities and generating a recommendation that can be used by the representatives to help salvage the opportunities. Historical information as well as machine learning algorithms are used to identify the failing opportunities by classifying new and currently in-pursuit opportunities using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk. Distances between opportunities are estimated based on local neighborhoods determined by relevant variables influencing those opportunities in the local neighborhood. The shortest distance between at risk opportunities and winning opportunities can be identified and utilized to generate the recommendation based on the relevant variables for the shortest path. In some embodiments, an ordered list of actions or changes to actions needed for a successful disposition of the opportunity may be generated and provided to the representative.

What is claimed is: 
     
         1 . A method for automatic opportunity evaluation and salvage, the method comprising:
 classifying a plurality of opportunities within a multi-dimensional space, wherein:
 each dimension of the multi-dimensional space is associated with a variable of a plurality of variables, 
 each variable of the plurality of variables is relevant to at least one of the plurality of opportunities, and 
 the classification comprises:
 grouping subsets of the plurality of opportunities into a plurality of local neighborhoods; and 
 assigning a score having a first value or a second value to each of the plurality of opportunities based on a model of the multi-dimensional space, wherein the first value indicates a positive outcome and the second value indicates a negative outcome; 
 
   identifying a subset of the plurality of variables in each of the plurality of local neighborhoods;   for a first opportunity of the plurality of opportunities having an indicator of the negative outcome and in a first local neighborhood of the plurality of local neighborhoods:
 identifying, based on at least one of the subset of the variables, a second opportunity within the first local neighborhood having the indicator of the positive outcome; 
 calculating a distance, based on associated variables of the subset of the plurality of variables for the first local neighborhood, between the first opportunity and the second opportunity having the indicator of the positive outcome; 
 executing a simulation to find a shortest path, based on the calculated distance, that changes the score for the first opportunity from the second value to the first value; and 
 generating a recommendation for the first opportunity based on the shortest path. 
   
     
     
         2 . The method of  claim 1 , wherein identifying the subset of the plurality of variables comprises selecting the subset of the plurality of variables for each local neighborhood having the greatest gradient change within the local neighborhood. 
     
     
         3 . The method of  claim 1 , wherein executing the simulation comprises identifying a list of the plurality of variables that, when changed for the first opportunity, changes the score for the first opportunity from the second value to the first value, and wherein generating the recommendation for the first opportunity is further based on the list of the plurality of variables. 
     
     
         4 . The method of  claim 1 , further comprising:
 generating a natural language message based on the recommendation to provide to a sales representative in a graphical user interface.   
     
     
         5 . The method of  claim 1 , wherein the grouping subsets of the plurality of opportunities into a plurality of local neighborhoods is based at least in part on at least one of a size of each opportunity, timing of each opportunity, one or more products involved in each opportunity, or similarities of activities in each opportunity. 
     
     
         6 . The method of  claim 1 , further comprising:
 capturing first episodic memory of at least one action performed by a first sales representative for the first opportunity;   capturing second episodic memory of at least one action performed by a second sales representative for the second opportunity; and   determining that the at least one action performed by the second sales representative is related to at least one of the associated variables used to calculate the distance between the first opportunity and the second opportunity,   wherein generating the recommendation is further based on the at least one action performed by the second sales representative that differs from the at least one action performed by the first sales representative.   
     
     
         7 . The method of  claim 1 , wherein assigning the score to the first opportunity comprises at least one of:
 comparing an activity level between the first opportunity with the second opportunity;   identifying a frequency of change of a projected close date of the first opportunity;   identifying a frequency of deal value change of the first opportunity; or   identifying a progression rate for the first opportunity.   
     
     
         8 . The method of  claim 1 , further comprising:
 calculating a boundary between opportunities having the first value and opportunities having the second value within the first local neighborhood; and   calculating a second distance, based on the associated variables of the subset of the plurality of variables for the first local neighborhood, between the first opportunity and the boundary,   wherein executing the simulation further comprises finding a second shortest path, based on the second distance, between the first opportunity and the boundary, and   wherein generating the recommendation for the first opportunity is further based on the second shortest path between the first opportunity and the boundary.   
     
     
         9 . A system, comprising
 one or more processors; and   a memory having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to:
 classify a plurality of opportunities within a multi-dimensional space, wherein:
 each dimension of the multi-dimensional space is associated with a variable of a plurality of variables, 
 each variable of the plurality of variables is relevant to at least one of the plurality of opportunities, and 
 the instructions to classify the plurality of opportunities comprise instructions that, when executed by the one or more processors, cause the one or more processors to:
 group subsets of the plurality of opportunities into a plurality of local neighborhoods; and 
 assign a score having a first value or a second value to each of the plurality of opportunities based on a model of the multi-dimensional space, wherein the first value indicates a positive outcome and the second value indicates a negative outcome; 
 
 
 identify a subset of the plurality of variables in each of the plurality of local neighborhoods; and 
 for a first opportunity of the plurality of opportunities having an indicator of the negative outcome and in a first local neighborhood of the plurality of local neighborhoods:
 identify, based on at least one of the subset of the variables, a second opportunity within the first local neighborhood having the indicator of the positive outcome; 
 calculate a distance, based on associated variables of the subset of the plurality of variables for the first local neighborhood, between the first opportunity and the second opportunity having the indicator of the positive outcome; 
 execute a simulation to find a shortest path, based on the calculated distance, that changes the score for the first opportunity from the second value to the first value; and 
 generate a recommendation for the first opportunity based on the shortest path. 
 
   
     
     
         10 . The system of  claim 9 , wherein the instructions to identify the subset of the plurality of variables comprises further instructions that, when executed by the one or more processors, cause the one or more processors to select the subset of the plurality of variables having the greatest gradient change within the neighborhood. 
     
     
         11 . The system of  claim 9 , wherein the instructions to execute the simulation search comprises further instructions that, when executed by the one or more processors, cause the one or more processors to identify a list of the plurality of variables that, when changed for the first opportunity, changes the score for the first opportunity from the second value to the first value, wherein generating the recommendation for the first opportunity is further based on the list of the plurality of variables. 
     
     
         12 . The system of  claim 9 , wherein the memory comprises further instructions that, when executed by the one or more processors, cause the one or more processors to:
 generate a natural language message based on the recommendation; and   transmit the natural language message to a device of a sales representative in a graphical user interface.   
     
     
         13 . The system of  claim 9 , wherein the grouping subsets of the plurality of opportunities into local neighborhoods is based at least in part on at least one of a size of each opportunity, timing of each opportunity, one or more products involved in each opportunity, or similarities of activities in each opportunity. 
     
     
         14 . The system of  claim 9 , wherein the memory comprises further instructions that, when executed by the one or more processors, cause the one or more processors to:
 capture first episodic memory of at least one action performed by a first sales representative for the first opportunity;   capture second episodic memory of at least one action performed by a second sales representative for the at least one opportunity within the same neighborhood having the indicator of the positive outcome; and   determine that the at least one action performed by the second sales representative is related to at least one of the associated variables,   wherein generating the recommendation is further based on the at least one action performed by the second sales representative that differs from the at least one action performed by the first sales representative.   
     
     
         15 . The system of  claim 9 , wherein assigning the first value or the second value to one of the plurality of opportunities comprises at least one of:
 compare an activity level between the one of the plurality of opportunities with a known opportunity having a known positive outcome;   identifying a frequency of change of a projected close date of the one of the plurality of opportunities;   identifying a frequency of deal value change of the one of the plurality of opportunities; or   identifying a progression rate for the one of the plurality of opportunities.   
     
     
         16 . The system of  claim 9 , wherein the memory comprises further instructions that, when executed by the one or more processors, cause the one or more processors to:
 calculate a boundary, within each of the plurality of local neighborhoods, between opportunities having the first value indicating a positive outcome and opportunities having the second value indicating a negative outcome; and   calculate a distance, based on associated variables of the plurality of variables, between the first opportunity and the boundary, wherein calculating the distance is based on the at least one of the subset of the variables for the local neighborhood,   wherein executing the simulation further comprises finding a shortest distance, based on the associated variables, between the first opportunity and the boundary, and   wherein generating the recommendation for the first opportunity is further based on the shortest distance between the first opportunity and the boundary.   
     
     
         17 . A computer-readable media having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to:
 classify a plurality of opportunities within a multi-dimensional space, wherein:
 each dimension of the multi-dimensional space is associated with a variable of a plurality of variables, 
 each variable of the plurality of variables is relevant to at least one of the plurality of opportunities, and 
 the instructions to classify the plurality of opportunities comprise instructions that, when executed by the one or more processors, cause the one or more processors to:
 group subsets of the plurality of opportunities into a plurality of local neighborhoods; and 
 assign a score having a first value or a second value to each of the plurality of opportunities based on a model of the multi-dimensional space, wherein the first value indicates a positive outcome and the second value indicates a negative outcome; 
 
   identify a subset of the plurality of variables in each of the plurality of local neighborhoods; and   for a first opportunity of the plurality of opportunities having an indicator of the negative outcome and in a first local neighborhood of the plurality of local neighborhoods:
 identify, based on at least one of the subset of the variables, a second opportunity within the first local neighborhood having the indicator of the positive outcome; 
 calculate a distance, based on associated variables of the subset of the plurality of variables for the first local neighborhood, between the first opportunity and the second opportunity having the indicator of the positive outcome; 
 execute a simulation to find a shortest path, based on the calculated distance, that changes the score for the first opportunity from the second value to the first value; and 
 generate a recommendation for the first opportunity based on the shortest path. 
   
     
     
         18 . The computer-readable media of  claim 17 , wherein the instructions to identify the subset of the plurality of variables comprises further instructions that, when executed by the one or more processors, cause the one or more processors to select the subset of the plurality of variables having the greatest gradient change within the neighborhood. 
     
     
         19 . The computer-readable media of  claim 17 , wherein the instructions to execute the simulation search comprises further instructions that, when executed by the one or more processors, cause the one or more processors to identify a list of the plurality of variables that, when changed for the first opportunity, changes the score for the first opportunity from the second value to the first value, wherein generating the recommendation for the first opportunity is further based on the list of the plurality of variables. 
     
     
         20 . The computer-readable media of  claim 17 , comprising further instructions that, when executed by the one or more processors, cause the one or more processors to:
 generate a natural language message based on the recommendation; and   transmit the natural language message to a device of a sales representative in a graphical user interface.